Detection scheme utilizing transmitter-supplied non-linearity data in the presence of transmitter non-linearity

ABSTRACT

Improved wireless communications are enabled. For example, a transmitter can characterize a response of non-linear devices associated with an active-array-antenna. Data indicative of the non-linear response is forwarded to a receiving device, which can utilize the data in conjunction with decoding wireless transmissions of the active-array-antenna. Improved bit error rates can be achieved when utilizing transmitter-provided non-linear response data, as provided herein.

TECHNICAL FIELD

The present application relates generally to the field of wirelesscommunication, and, for example, to transmitter-mapped nonlinearity datautilized for decoding multiple input multiple output (MIMO) wirelesstransmissions.

BACKGROUND

Radio technologies in cellular communications have grown rapidly andevolved since the launch of analog cellular systems in the 1980s,starting from the First Generation (1G) in 1980s, Second Generation (2G)in 1990s, Third Generation (3G) in 2000s, and Fourth Generation (4G) in2010s (including Long Term Evolution (LTE) and variants of LTE). Fifthgeneration (5G) access networks, which can also be referred to as NewRadio (NR) access networks, are currently being developed and expectedto fulfill the demand for exponentially increasing data traffic, and tohandle a very wide range of use cases and requirements, including amongothers mobile broadband (MBB) and machine type communications (e.g.,involving Internet of Things (IOT) devices).

The upcoming 5G access network may utilize higher frequencies (e.g., >6GHz) to aid in increasing capacity. Currently, much of the millimeterwave (mmWave) spectrum, the band of spectrum between 30 gigahertz (GHz)and 300 GHz is underutilized. The millimeter waves have shorterwavelengths that range from 10 millimeters to 1 millimeter, and thesemmWave signals experience severe path loss, penetration loss, andfading. However, the shorter wavelength at mmWave frequencies alsoallows more antennas to be packed in the same physical dimension, whichallows for large-scale spatial multiplexing and highly directionalbeamforming.

Performance can be improved if both the transmitter and the receiver areequipped with multiple antennas. Multi-antenna techniques cansignificantly increase the data rates and reliability of a wirelesscommunication system. The use of multiple input multiple output (MIMO)techniques, which was introduced in the third-generation partnershipproject (3GPP) and has been in use (including with LTE), is amulti-antenna technique that can improve the spectral efficiency oftransmissions, thereby significantly boosting the overall data carryingcapacity of wireless systems. MIMO techniques can improve mmWavecommunications, and has been widely recognized a potentially importantcomponent for access networks operating in higher frequencies. MIMO canbe used for achieving diversity gain, spatial multiplexing gain andbeamforming gain. For these reasons, MIMO systems, including massiveMIMO systems using a large number of antennas, can be an important partof the 3rd and 4th generation wireless systems, and are planned for usein 5G systems.

The above-described background relating to wireless networks is merelyintended to provide a contextual overview of some current issues, and isnot intended to be exhaustive. Other contextual information may becomefurther apparent upon review of the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the subject disclosureare described with reference to the following figures, wherein likereference numerals refer to like parts throughout the various viewsunless otherwise specified.

FIG. 1 illustrates a sample wireless communication system with a networknode device that communicates with a user equipment in one or moreembodiments.

FIG. 2 depicts a block diagram of an example transmit antenna systemhaving a non-linear device according to one or more embodiments of thepresent disclosure.

FIG. 3 depicts a block diagram of an example non-linear response of anexample non-linear device, in an embodiment.

FIG. 4 illustrates an example power spectral density graph for awireless transmission utilizing a non-linear power amplifier, in supportof aspects of the disclosure.

FIG. 5 depicts a sample bit error rate at a receiving device accordingto one or more embodiments of the present disclosure.

FIG. 6 illustrates a block diagram of an example transmit antenna systemfor a multiple-input, multiple-output antenna arrangement in furtherembodiments.

FIG. 7 illustrates a block diagram of an example embodiment oftransmitter-provided non-linearity data utilizing digitalpre-distortion.

FIG. 8 depicts a block diagram of a sample embodiment of a MIMOreceiving device utilizing transmitter-provided non-linearity data forsignal processing.

FIG. 9 illustrates a table of an example format for data transmission ofnon-linearity data, according to additional aspects of the presentdisclosure.

FIG. 10 shows a flowchart of an example method for achieving improvedsignal processing for wireless communication according to otherembodiments.

FIG. 11 depicts a flowchart of a sample method for achieving improvedsignal processing in a massive MIMO wireless communication environmentin still other embodiments.

FIG. 12 illustrates a flowchart of an example method for facilitatingimproved signal processing for a receiving device, in an embodiment.

FIG. 13 shows a flowchart of a sample method for facilitating improvedsignal processing utilizing transmitter-provided non-linearity data, inyet other embodiments.

FIG. 14 illustrates an example block diagram of an example mobilehandset, which can be a UE, in accordance with various embodiments ofthe subject disclosure.

FIG. 15 illustrates an example block diagram of a computer, e.g., anetwork node, which can be operable to execute processes and methodsdisclosed herein.

DETAILED DESCRIPTION

The following description and the annexed drawings set forth in detailcertain illustrative aspects of the subject matter. However, theseaspects are indicative of but a few of the various ways in which theprinciples of the subject matter can be implemented or employed. Otheraspects, advantages, and novel features of the disclosed subject matterwill become apparent from the following detailed description whenconsidered in conjunction with the provided drawings. In the followingdescription, for purposes of explanation, numerous specific details areset forth to provide an understanding of the subject disclosure. It maybe evident, however, that the subject disclosure may be practicedwithout these specific details. In other instances, well-knownstructures and devices are shown in block diagram form in order tofacilitate describing the subject disclosure. For example, the methods(e.g., processes and logic flows) described in this specification can beperformed by devices (e.g., a user equipment (UE), a network nodedevice, etc.) comprising programmable processors that execute machineexecutable instructions to facilitate performance of operationsdescribed herein. Examples of such devices can be devices comprisingcircuitry and components as described in FIG. 13 and FIG. 14, infra.

The present patent application relates at a high level to characterizinga non-linear response(s) of a device(s) connected with a transmitamplifier, and providing data to recreate the non-linear response to areceiving device. The non-linear response can be characterized at atransmitter device, which determines an output of the non-lineardevice(s) as a function of an input to the device(s). Thischaracterization at the transmitter device, when forwarded to thereceiving device, enables increased efficiency and accuracy in signalprocessing for the receiving device, enhancing channel filtering andimproving bit error rates. In some embodiments, the non-linear responsecan be characterized by fitting a basis function to the non-linearresponse and providing coefficients and indicia indicative of the basisfunction to a receiving device. The receiving device can recreate thenon-linear response function and utilize this function in decoding areceived wireless transmission. In various embodiments, atransmitter-supplied non-linear response can be employed in decoding amultiple-input, multiple-output (MIMO) wireless transmission (e.g., amassive MIMO transmission utilizing many wireless antennas employed fora 5G wireless system), in lieu of or in addition to other signalprocessing techniques. For instance, in some embodiments thetransmitter-supplied non-linear response function can be employed inlieu of digital pre-distortion of an input signal to a non-lineardevice. In other embodiments, the transmitter-supplied non-linearresponse function can be implemented in conjunction with digitalpre-distortion (DPD) techniques, which can be characterized with thenon-linear response. In further embodiments, the non-linear response canbe a static response, transmitted during call setup signaling orphysical layer transmissions, or can be a dynamic response transmitted(and updated) at physical layer transmissions.

FIG. 1 illustrates an example mobile communication system 100 (alsoreferred to as mobile system 100) in accordance with various aspects andembodiments of the subject disclosure. In example embodiments (alsoreferred to as non-limiting embodiments), mobile system 100 can comprisea mobile (also referred to as cellular) network 106, which can compriseone or more mobile networks typically operated by communication serviceproviders (e.g., mobile network 106). The mobile system 100 can alsocomprise one or more user equipment (UE) 102 _(1-n) (also referred to asuser devices, where n is a suitable integer greater than 0). The UEs 102_(1-n) can communicate with one another via one or more network nodedevices (also referred to as network nodes) 104 _(1-n) (referred to asnetwork node 104 in the singular) of the mobile network 106. The dashedarrow lines from the network nodes 104 _(1-n) to the UE 102 _(1-n)represent downlink (DL) communications and the solid arrow lines fromthe UE 102 _(1-n) to the network nodes 104 _(1-n) represent uplink (UL)communications.

UE 102 _(1-n) can comprise, for example, any type of device that cancommunicate with mobile network 106, as well as other networks (seebelow). The UE 102 _(1-n) can have one or more antenna panels havingvertical and horizontal elements. Examples of a UE 102 _(1-n) comprise atarget device, device to device (D2D) UE, machine type UE, or UE capableof machine to machine (M2M) communications, personal digital assistant(PDA), tablet, mobile terminal, smart phone, laptop mounted equipment(LME), universal serial bus (USB) dongles enabled for mobilecommunications, a computer having mobile capabilities, a mobile devicesuch as cellular phone, a dual mode mobile handset, a laptop havinglaptop embedded equipment (LEE, such as a mobile broadband adapter), atablet computer having a mobile broadband adapter, a wearable device, avirtual reality (VR) device, a heads-up display (HUD) device, a smartcar, a machine-type communication (MTC) device, and the like. UE 102_(1-n) can also comprise IOT devices that communicate wirelessly.

Mobile network 106 can include various types of disparate networksimplementing various transmission protocols, including but not limitedto cellular networks, femto networks, picocell networks, microcellnetworks, internet protocol (IP) networks, Wi-Fi networks associatedwith the mobile network (e.g., a Wi-Fi “hotspot” implemented by a mobilehandset), and the like. For example, in at least one implementation,mobile network 100 can be or can include a large scale wirelesscommunication network that spans various geographic areas, and comprisevarious additional devices and components (e.g., additional networkdevices, additional UEs, network server devices, etc.).

Still referring to FIG. 1, mobile network 106 can employ variouscellular systems, technologies, and modulation schemes to facilitatewireless radio communications between devices (e.g., the UE 102 _(1-a)and the network node 104). While example embodiments might be describedfor 5G new radio (NR) systems, the embodiments can be applicable to anyradio access technology (RAT) or multi-RAT system where the UE operatesusing multiple carriers. For example, mobile system 100 can be of anyvariety, and operate in accordance with standards, protocols (alsoreferred to as schemes), and network architectures, including but notlimited to: global system for mobile communications (GSM), 3GSM, GSMEnhanced Data Rates for Global Evolution (GSM EDGE) radio access network(GERAN), Universal Mobile Telecommunications Service (UMTS), GeneralPacket Radio Service (GPRS), Evolution-Data Optimized (EV-DO), DigitalEnhanced Cordless Telecommunications (DECT), Digital AMPS (IS-136/TDMA),Integrated Digital Enhanced Network (iDEN), Long Term Evolution (LTE),LTE Frequency Division Duplexing (LTE FDD), LTE time division duplexing(LTE TDD), Time Division LTE (TD-LTE), LTE Advanced (LTE-A), TimeDivision LTE Advanced (TD-LTE-A), Advanced eXtended Global Platform(AXGP), High Speed Packet Access (HSPA), Code Division Multiple Access(CDMA), Wideband CDMA (WCMDA), CDMA2000, Time Division Multiple Access(TDMA), Frequency Division Multiple Access (FDMA), Multi-carrier CodeDivision Multiple Access (MC-CDMA), Single-carrier Code DivisionMultiple Access (SC-CDMA), Single-carrier FDMA (SC-FDMA), OrthogonalFrequency Division Multiplexing (OFDM), Discrete Fourier TransformSpread OFDM (DFT-spread OFDM), Single Carrier FDMA (SC-FDMA), FilterBank Based Multi-carrier (FBMC), zero tail DFT-spread-OFDM (ZTDFT-s-OFDM), Unique Word OFDM (UW-OFDM), Unique Word DFT-spread OFDM (UWDFT-Spread-OFDM), Cyclic Prefix OFDM (CP-OFDM), resource-block-filteredOFDM, Generalized Frequency Division Multiplexing (GFDM), Fixed-mobileConvergence (FMC), Universal Fixed-mobile Convergence (UFMC), MultiRadio Bearers (RAB), Wi-Fi, Worldwide Interoperability for MicrowaveAccess (WiMax), and the like.

Still referring to FIG. 1, in example embodiments, UE 102 _(1-n) can becommunicatively coupled (or in other words, connected) to a network node104 of the mobile network 106. Network node 104 can have a cabinet andother protected enclosures, an antenna mast, and multiple antennas forperforming various transmission operations (e.g., MIMO operations). Eachnetwork node 104 can serve several cells, or sectors, depending on theconfiguration and type of antenna. Network node 104 can comprise NodeBdevices, base station (BS) devices, mobile stations, access point (AP)devices, and radio access network (RAN) devices. Network node 104 canalso include multi-standard radio (MSR) radio node devices, includingbut not limited to: an MSR BS, an eNode B device (e.g., evolved NodeB),a network controller, a radio network controller (RNC), a base stationcontroller (BSC), a relay, a donor node controlling relay, a basetransceiver station (BTS), an access point, a transmission point (TP), atransmission/receive point (TRP), a transmission node, a remote radiounit (RRU, described further below), a remote radio head (RRH), nodes indistributed antenna system (DAS), and the like. In 5G terminology, thenetwork node is referred to by some as a gNodeB device.

Still referring to FIG. 1, in various embodiments, mobile network 106can be configured to provide and employ 5G cellular networking featuresand functionalities. 5G wireless communication networks are expected tofulfill the demand of exponentially increasing data traffic and to allowpeople and machines to enjoy gigabit data rates with virtually zerolatency. Compared to 4G, 5G supports more diverse traffic scenarios. Forexample, in addition to the various types of data communication betweenconventional UEs (e.g., phones, smartphones, tablets, PCs, televisions,Internet enabled televisions, etc.) supported by 4G networks, 5Gnetworks can be employed to support data communication between smartcars in association with driverless car environments, as well as machinetype communications (MTCs). Considering the different communicationneeds of these different traffic scenarios, the ability to dynamicallyconfigure waveform parameters based on traffic scenarios while retainingthe benefits of multi carrier modulation schemes (e.g., OFDM and relatedschemes) can provide a significant contribution to the highspeed/capacity and low latency demands of 5G networks. With waveformsthat split the bandwidth into several sub-bands, different types ofservices can be accommodated in different sub-bands with the mostsuitable waveform and numerology, leading to an improved spectrumutilization for 5G networks.

Still referring to FIG. 1, to meet the demand for data centricapplications, features of proposed 5G networks may comprise: increasedpeak bit rate (e.g., 20 Gbps), larger data volume per unit area (e.g.,high system spectral efficiency—for example about 3.5 times that ofspectral efficiency of long term evolution (LTE) systems), high capacitythat allows more device connectivity both concurrently andinstantaneously, lower battery/power consumption (which reduces energyand consumption costs), better connectivity regardless of the geographicregion in which a user is located, a larger numbers of devices, lowerinfrastructural development costs, and higher reliability of thecommunications. Thus, 5G networks may allow for: data rates of severaltens of megabits per second should be supported for tens of thousands ofusers, 1 Gbps to be offered simultaneously to tens of workers on thesame office floor, for example; several hundreds of thousands ofsimultaneous connections to be supported for massive sensor deployments;improved coverage, enhanced signaling efficiency; reduced latencycompared to LTE, among others.

MIMO systems are expected to employ active-array-antenna systems inwhich radio frequency (RF) devices such as power amplifiers,transceivers, analog to digital converters and other electronic devicesare integrated with an array of antenna elements. These active arrayantenna systems provide several benefits over passive antenna systems inwhich antennas are connected to transceivers through feeder cables.These benefits include simplified installation, reduction in cablelosses, reduced energy consumption and improved performance. Moreover,active antenna systems can be effective in implementing cell specificbeamforming, user specific beamforming, vertical sectorization, massiveMIMO, elevation beamforming, and so on. For 5G implementations,requirements and test methodologies have been initiated forfull-dimensional MIMO (FD-MIMO) systems involving 16, 32 or 64 activearray antennas to achieve the high service and efficiency goals proposedfor 5G networks.

Referring now to FIG. 2, a block diagram of an example transmit antennasystem 200 is illustrated, according to one or more aspects of thepresent disclosure. Transmit antenna system 200 can be anactive-array-antenna system, in various embodiments. Moreover, transmitantenna system 200 can facilitate improved signal processing to achievefavorable spectral density and bit error rates at an associated receiverdevice (not depicted, but see, e.g., UE 102 _(1-n) of FIG. 1, supra, ormultiple-input, multiple-output (MIMO) receiving device 820 of FIG. 8,infra). In further embodiments, antenna system 200 can replace othersignal processing techniques that have high power consumption andprocessing overhead, the costs of which can be additive in systems withlarge numbers of antennas (e.g., massive MIMO systems), to furthermagnify these benefits.

Transmit antenna system 200 can comprise one or more non-linear devices202 operably connected to an antenna 206 (e.g., a transmit antenna, butalso a receive antenna). Non-linear device(s) 202 receives a basebandsignal comprising signal processed input bits 208 from a baseband device204. Non-linear device(s) 202 generates a non-linear device output thatis provided to antenna 206. The non-linear device output can varydepending on a type and function of non-linear device(s) 202. Forinstance, in an embodiment where non-linear device(s) 202 is a signalamplifier, the non-linear device output can be a non-linear amplifiedsignal. The subject disclosure is not so limited, however, and in otherembodiments non-linear device(s) 202 can be multiple power amplifiers,one or more digital to analog converters (DACs), one or more analog todigital converters (ADCs), one or more local oscillators, or one or moreother non-linear devices known in the art, or a suitable combination ofthe foregoing. Where non-linear device(s) 202 comprises multiplenon-linear devices, the non-linear device output can be an aggregatednon-linear output of these devices in some embodiments, separatenon-linear device outputs of each device, or an aggregate of some deviceoutputs and a separate non-linear device output for other such devices.

The baseband signal and the non-linear device output is provided to anon-linear model identifier 210. Non-linear model identifier 210 cancomprise a processor 212 and memory 214 to store executable instructionsthat, when executed by the processor, facilitate performance ofoperations of non-linear model identifier 210. These operations caninclude identifying (e.g., estimating) the non-linearity (e.g., anon-linear response) of the non-linear device output as a function F(x)of the baseband signal (for a more detailed discussion of the non-linearresponse of a MIMO communication with t transmit antennas and r receiveantennas, see FIG. 8, infra).

Identifying non-linearity of non-linear device(s) 202 can be implementedusing mathematical characterization of the non-linear device output as afunction of the baseband signal. Examples can include a polynomial fitbetween an input signal (x) (e.g., the baseband signal, or input bits208 in an embodiment) to non-linear device(s) 202, and output signal (y)of non-linear device(s) 202, or y=f(x). Characterized differently,non-linear model identifier 210 can estimate the function f(.) thatrepresents the non-linear response of non-linear device(s) 202 (e.g.,see FIG. 3, infra, for an example non-linear response of an examplepower amplifier, for illustration). Non-linear model identifier 210 canemploy various algorithms for estimating the non-linear response f(.) ofnon-linear device(s) 202, including algorithms based on criterionminimization, e.g., solving a least squares problem, or can be based onBayesian techniques where Kalman filtering is employed, or otheralgorithms known in the art for characterizing non-linear functions. Inat least one embodiment, non-linear model identifier 210 can estimate aseparate function f(.)_(n′) for each of n′ non-linear device(s) operablycoupled to one of n′ antennas, where n′ is a suitable integer greaterthan 1 (e.g., see FIG. 7, infra). In other embodiments, e.g., where thenon-linear device per antenna branch is the same, or has a same orreasonably same non-linear function f(.) (e.g., where f(.)_(n′) is thesame or substantially the same for each of the n′ non-linear devices),non-linear model identifier can estimate the non-linear responsefunction f(.) for one such device and impute the function to eachantenna branch. Non-linear model identifier can generate non-linearfunction data characterizing the non-linear response function ofnon-linear device(s) 202, and provide the non-linear function data in anon-linear function data message 216 for transmission to a receivingdevice.

In general, wireless communications employ a channel encoder and channeldecoder to improve bit error rate performance. Most channel decoders aresoft input and soft output (SISO) decoders, or soft input hard output(SIHO) decoders. In both cases, a receiving device employs soft inputinformation to determine the bits received in a received wirelesstransmission. Because a non-linear response of an active array antennacan affect transmitted, and thus received, wireless signals, thereceiving device can utilize the non-linear function data provided innon-linear function data message 216 at least in part to generate softinformation for decoding wireless transmissions of transmit antennasystem 200. As a result, improved bit error performance can be achieved(e.g., see FIG. 4, infra).

FIG. 3 illustrates an example graph of a non-linear response 300 of apower amplifier non-linear device, according to various disclosedembodiments. The graph of FIG. 3 plots normalized input magnitude(before power amplification) on the horizontal axis, and normalizedoutput magnitude (after power amplification) on the horizontal axis. Asis evident from the graph, non-linear response 300 has a significantnon-linearity. One side effect of a non-linear device on wirelesstransmissions is signal leakage outside of a target frequency band. Anexample of this phenomenon is illustrated by FIG. 4, which depicts asecond graph of power spectral density 400 charting normalized frequency[f/f_(s)] versus power spectral density [dB/Hz]. As is evident from FIG.4, power spectral density of transmissions having an ideal poweramplifier achieves sharp cutoff outside a target frequency range,whereas the power spectral density of transmissions having a non-linearpower amplifier leak significantly into adjacent frequencies. Signalleakage outside the target frequency range can have deleterious effectson efficacy of wireless communications, particularly for MIMOcommunications. The impact is such that the advantages of MIMOtechniques, including diversity gain and multiplexing gain, arediminished.

FIG. 5 illustrates a third graph of estimated bit error rate performance500 at a receiving device in response to transmitter-supplied non-linearresponse data. The graph plots Eb/No in dB on the horizontal axis versusbit error rate on the vertical axis. The bit error rate performance atthe receiving device is greatly improved in response to a receivingdevice employing transmitter-supplied non-linearity data as compared tothe bit error rate performance of a non-linear device without signalprocessing enhancements. Bit error rate performance 500 illustrates thesignificant estimated benefit to the bit error rate performance providedby embodiments of the present disclosure.

FIG. 6 illustrates a block diagram of an example multiple-antenna system600 for MIMO downlink communications, in one or more embodiments.Multiple-antenna system 600 includes a first transmit antennae 606Athrough an Nth transmit antenna_(N) 606B, where N is a suitable integergreater than one. Respective antenna systems 606A, 606B comprise one ormore non-linear devices 602A, 602B and baseband devices 604A, 604B thatreceive input bits 208 and provide respective baseband signals to thenon-linear devices 602A, 602B. A non-linear model identifier 210receives the respective baseband signals and non-linear responses of thenon-linear devices 602A, 602B and generates respective non-linearfunction responses for each of the non-linear devices 602A, 602B.Respective non-linear function data_(1-N) can be included in messages612A, 612B and transmitted to a receiving device for processing awireless transmission received from multiple-antenna system 600. In anembodiment, an aggregator 620 can be employed to generate an aggregateof the respective non-linear function data for non-linear devices 602A,602B, which can then be sent to the receiving device. In otherembodiments, where non-linear devices 602A, 602B are substantially thesame, or their non-linear function response functions f(.) aresubstantially the same, a representative one of non-linear functiondata_(1-N) can be provided to the receiving device.

As a general explanation, take the case of a MIMO system with N_(t)transmit antennas and N_(r) receive antennas, where N_(t) and N_(r) arerespective integers greater than 1. (Note that a bold symbol is utilizedherein to represent a vector or matrix). A received signal vector Y at areceiving device can be expressed by equation (1) as:r=HPu+n  (1)where r is of size N_(r)×1, H is the complex channel matrix of sizeN_(r)×N_(t), P is the precoding matrix of size N_(t)×N_(L), where N_(L)is the number of transmission layers (also referred to as rank), u isthe transmission vector of size N_(L)×1 and n is the noise vector ofsize N_(r)×1. According to equation (1), the transmission vector u iscomputed at an output of a non-linear device (e.g., a power amplifier,an oscillator, a DAC, etc.), using an identified static model of thenon-linear device. The power amplifier is characterized by u=f(x), wherex is the symbol vector input to the non-linear device (e.g., beforepower amplification, etc.) and f(.) is the non-linear function of thenon-linear device. The received signal vector can be rewritten byequation (2) as follows:r=HPf(x)+n  (2)by substitution for u.

The receiving device can employ the maximum likelihood detectionalgorithm that minimizes the probability of sequence error as follows:P _(a) =P(x≠{circumflex over (x)})  (3)where {circumflex over (x)} is the estimate of x at the receiver.Equation (3) can be rewritten asP _(s) =P(f(x)≠{circumflex over (f)}({circumflex over (x)}))noting that minimizing P_(s) is then approximately equivalent tomaximizing the probability of correctly estimating x, which can beexpressed as follows:

${P_{s} = {\underset{\hat{f}{(\hat{x})}}{\arg\;\max}{P\left( {{{f(x)} = {{\hat{f}\left( \hat{x} \right)}❘Y}},{HP}} \right)}}},$Applying Baye's theorem yields:

${P\left( {{{f(x)} = {{\hat{f}\left( \hat{x} \right)}❘r}},{HP}} \right)} = {\frac{p_{{r❘{f{(x)}}},{HP}}\left( {{{r❘{f(x)}} = {\hat{f}\left( \hat{x} \right)}},{HP}} \right)}{p_{r❘{HP}}\left( {r❘H} \right)} \cdot {P\left( {{f(x)} = {\hat{f}\left( \hat{x} \right)}} \right)}}$where p_(r)|f(x) and p_(r)|HP are the conditional probability densityfunctions of r given (f(x), HP) and HP, respectively. From the aboveequation, p_(r)|HP(r|HP) is independent of the applied hypothesis{circumflex over (f)}({circumflex over (x)}). In addition, for equallylikely sequences, P(f(x)={circumflex over (f)}({circumflex over (x)}))is independent of {circumflex over (f)}({circumflex over (x)}), andtherefore these two terms do not affect the selection of {circumflexover (x)}. As a result, the following is obtained:

$P_{s} = {\underset{\hat{f}{(\hat{x})}}{\arg\;\max}{p_{{r❘{f{(x)}}},{HP}}\left( {{{r❘{f(x)}} = {\hat{f}\left( \hat{x} \right)}},{HP}} \right)}}$which can be rewritten as:p _(r) |f(x),HP(r|f(x)={circumflex over (f)}({circumflex over(x)}),HP)=p _(n)(r−HP{circumflex over (f)}({circumflex over (x)}))where p_(n) is the Gaussian probability density function. For additivewhite Gaussian noise (AWGN) channels, maximixing p_(n) is equivalent tominimizing ∥r−HP{circumflex over (f)}({circumflex over (x)})∥².Accordingly, the maximum likelihood estimate of x is given by equation(4):

$\begin{matrix}{{\hat{x}}_{ML} = {\underset{\hat{f}{(\hat{x})}}{\arg\;\min}{{r - {{HP}\;{\hat{f}\left( \hat{x} \right)}}}}^{2}}} & (4)\end{matrix}$A receiving device can employ a maximum likelihood detector to choosethe message {circumflex over (x)} as the one giving the smallestdistance between the received vector r and the hypothesized messageHP{circumflex over (f)}({circumflex over (x)}).

As stated previously, a receiving device according to the subjectdisclosure can utilize non-linear function data_(1-N) in messages 612A,612B at least in part to determine the maximum likelihood estimation ofthe received message bits. In further embodiments, the non-linearfunction data can be utilized in conjunction with channel informationand precoding information for determining the maximum likelihoodestimation of the received message bits.

FIG. 7 depicts a block diagram of an example MIMO transmission withdigital pre-distortion 700 according to alternative or additionalembodiments of the present disclosure. MIMO transmission with digitalpre-distortion 700 can employ multiple antennas 606A, 606B connected torespective non-linear devices 602A, 602B (or respective groups ofnon-linear devices 602A, 602B). A set of input bits 208 are provided toa baseband device 604A, 604B for each antenna 606A, 606B, whichrespectively output a baseband signal. The baseband signals are providedto DPD devices 704A, 704B, which distort the baseband signals and outputthe distorted baseband signals to the non-linear devices 602A, 602B. Inan embodiment, DPD devices 704A, 704B can comprise signal linearizationcircuitry, or the like.

Non-linear devices 602A, 602B can be configured to identify dynamic (asopposed to static) non-linear relation between an output of nonlineardevice(s) 602A, 602B and the baseband input to DPD devices 704A, 704B.In an embodiment, non-linear model identifier 710 can be substantiallysimilar to non-linear model identifier 210, described above. Inalternative or additional embodiments, non-linear model identifier 710can employ, e.g., a finite impulse response (FIR) filter, or state spacemodel in conjunction with fitting a basis function to the non-linearresponse of DPD devices 704A, 704B and non-linear devices 602A, 602B toidentify the dynamic non-linear response functions of such devices. Oncegenerated, non-linear function data can be provided in a message 712transmitted to a receiving device to facilitate decoding of a signaltransmitted by MIMO transmission with digital pre-distortion 700,utilizing the non-linear function data. Employing the digitalpre-distortion in conjunction with the transmitter-supplied non-linearfunction data can potentially further improve bit error rates at thereceiving device, without significant increase in the power andprocessing overhead required to implement the digital pre-distortion ofthe baseband signal.

FIG. 8 illustrates a block diagram of an example MIMO communication 800according to alternative or additional embodiments of the presentdisclosure. MIMO communication 800 can comprise a MIMO transmit device802 and a MIMO receiving device 820 communicatively coupled over awireless link 815. MIMO transmit device 802 can comprise multipleantennas 804 connected to one or more non-linear devices 808. Non-lineardevices 808 can comprise a non-linear power amplifier(s), a non-linearoscillator(s), a non-linear DAC, or the like, or a suitable combinationof the foregoing. A non-linear model identifier 806 is provided to map(e.g., estimate) a non-linear response of non-linear devices 808. Themapping can be via a polynomial fit (e.g., criterion minimizationsolving a least squares problem, Bayesian techniques utilizing Kalmanfilter, and so forth), or other suitable mathematical characterization,that models a non-linear function response of non-linear devices 808.

Once generated, the non-linear function response is provided in amessage 810 to MIMO receiving device 820. Message 810 can be transmittedas part of call setup signaling establishing the wireless link 815between MIMO transmit device 802, and MIMO receiving device 820, in anembodiment. In other embodiments, message 810 can be transmitted as partof physical layer signaling over wireless link 815.

MIMO receiving device 820 can be a wireless receiver, a user equipment(e.g., UE 102 _(1-n) of FIG. 1, supra), a mobile wireless device, and soforth. As illustrated, MIMO receiving device 820 can comprise aplurality of receive antennas 822 for acquiring wireless transmissionson wireless link 815. Additionally, MIMO receiving device 820 cancomprise a processor(s) 824, and memory 826 that stores executableinstructions that, when executed by processor(s) 824, facilitateperformance of operations of MIMO receiving device 820. Memory 826 cancomprise a data storage 834, in an embodiment, storing non-linear usagecode 832 comprising executable instructions for employing non-linearfunction data in conjunction with processing a received wireless signal.In an embodiment, the operations can comprise utilizing the non-linearfunction data, at least in part, as soft input information fordetermining a maximum likelihood estimation of data bits within thereceived wireless signal.

A signal processing device(s) 828 can obtain the received signal fromreceiving antennas 822. A non-linear data acquisition device 830 canobtain nonlinear function data provided in message 810 (e.g., accordingto message decoding instructions provided in non-linear usage code 832,in an embodiment). Message 810 can be acquired from signalingtransmissions during setup of wireless link 815, in an embodiment. Inother embodiments, message 810 can be acquired from physical layer datatransmissions of wireless link 815. Once received, non-linear functiondata within message 810 is extracted and utilized in conjunction withdecoding the received wireless signal from receiving antennas 822. Invarious embodiments, the non-linear function data is utilized inconjunction with channel information and precoding information todetermine maximum likelihood estimates for received data, as describedherein.

FIG. 9 illustrates an example non-linear data model 900 and format fortransmission, according to one or more additional embodiments of thepresent disclosure. Non-linear data model 900 can include identifyingindicia of a basis function mapped to a non-linear response of anon-linear device(s) utilized in an active-array-antenna arrangement.The identifying indicia can be suitable to enable a receiving device torecreate the basis function mapped to the non-linear response, and as aresult generate a high accuracy estimation of the non-linear response toachieve good signal decoding with improved bit error rate performance,in wireless communications.

Identifying indicia for non-linear data model 900 can include a modeltype 902, indicating a type of basis function utilized to mathematicallycharacterize the non-linear response of the non-linear device(s).Examples specified include a voltera series, a polynomial and a memorypolynomial, although other mathematical basis functions known in the artor made known to one of ordinary skill in the art by way of the contextprovided herein are considered within the scope of the presentdisclosure. An index 904 can alternatively or additionally be specifiedto refer to the type of basis function (rather than specify the basisfunction explicitly, in an embodiment). An order 906 of the basisfunction can be provided, as well as a memory length 908 in someembodiments. Moreover, coefficients of the basis function utilized tomap the basis function to the non-linear response are also includedwithin non-linear data model 900.

The aforementioned diagrams have been described with respect tointeraction between systems, networks, antenna arrays, wired or wirelessdevices, or the like. It should be appreciated that such diagrams caninclude those systems, antenna arrays or devices specified therein, someof the specified systems, antenna arrays or devices, or additional suchentities. For example, mobile communication system 100 could includemobile network 106 and MIMO receiving device 820, separate from orincluded within UEs 102 _(1-n), in conjunction with network node 104 ₁,which could include antenna system 200, as one of several possibleexamples, as would be recognized by one of skill in the art given thecontext provided by this disclosure. Sub-components could also beimplemented as components communicably connected to other sub-componentsrather than included within a parent component. Additionally, it shouldbe noted that two or more components could be combined into a singlecomponent providing aggregate functionality. For instance, non-linearmodel identifier 210 of FIG. 6 can be separate devices connected torespective antenna systems_(1-n) 606A, 606B, or can be a single deviceconnected to each of antenna systems_(1-n) 606A, 606B, optionallyincorporating aggregator 620 within such device(s). Components of thedisclosed networks, systems, antenna arrays or devices can also interactwith one or more other components not specifically described herein butknown by those of skill in the art, or made known to one of skill in theart by way of the context provided herein.

Methods in accordance with the disclosed subject matter are providedherein and illustrated as flowcharts. While, for purposes of simplicityof explanation, the methods are shown and described as a series of acts,it is to be understood and appreciated that the disclosed subject matteris not limited by the order of acts, as some acts may occur in differentorders and/or concurrently with other acts from that shown and describedherein. For example, those skilled in the art will understand andappreciate that a method could alternatively be represented as a seriesof interrelated states or events, such as in a state diagram. Moreover,not all illustrated acts may be required to implement a method inaccordance with the disclosed subject matter. Additionally, it should befurther appreciated that the methods disclosed hereinafter andthroughout this specification are capable of being stored on an articleof manufacture to facilitate transporting and transferring such methodsto computers.

In accordance with some example embodiments, a computing device (e.g.,network node 104, antenna system 200, non-linear model identifier 210,and so forth) can be operable to perform example methods and operations,as illustrated in flow diagrams as shown in FIGS. 10-13 and described inthe corresponding text, in accordance with various aspects andembodiments of the subject disclosure. Additionally, machine-readablestorage medium, comprising executable instructions that, when executedby a processor, can also facilitate performance of the methods andoperations described in FIGS. 10-13.

In non-limiting embodiments (also referred to as example embodiments), anetwork device (e.g., network node 104), comprising a processor and amemory that stores executable instructions that, when executed by theprocessor, facilitate performance of example operations 1000, as shownin FIG. 10. The network device can comprise antennas, and be operable touse the antennas to communicate via a massive multiple in multiple outprotocol.

The operations can comprise, at 1002, obtaining, by a wireless receiverof a system comprising a processor, signal data transmitted by awireless transmitter indicative of a non-linear response of a non-lineardevice operatively coupled with the wireless transmitter. The wirelesstransmitter can be a MIMO antenna system, in an embodiment. Further, thesignal data can include data indicative of a basis function fit to thenon-linear response of the non-linear device. In various embodiments,the non-linear device can comprise a power amplifier, a DAC, anoscillator, or other suitable non-linear device suitable for use inconjunction with a wireless transmitter.

At 1004, method 1000 can comprise storing, by the system, the signaldata. At 1006, method 1000 can comprise decoding, by the system, awireless transmission of the wireless transmitter comprising utilizingthe signal data indicative of the non-linear response of the non-lineardevice. In various embodiments, the wireless transmitter is a member ofa MIMO antenna arrangement comprising multiple wireless antennas. Infurther embodiments, the wireless transmission is a MIMO wirelesstransmission implemented by the multiple wireless antennas.

In an embodiment, decoding the wireless transmission can furthercomprise utilizing the signal data in conjunction with a maximumlikelihood function to determine data received with the wirelesstransmission. In further embodiments, the signal data can comprisecoefficients of a basis function fit to the non-linear response of thenon-linear device at the wireless transmitter. In one or more additionalembodiments, the method can comprise reconstructing, by the system, thebasis function fit to the non-linear response utilizing the coefficientsof the basis function.

In alternative embodiments, method 900 can comprise obtaining, by thesystem, the signal data from call setup signaling associated with theinitiation of the wireless transmission. In a further alternativeembodiment, the signal data represents an aggregate of non-linearresponses of a group of non-linear devices, comprising the non-lineardevice, respectively operatively coupled to the multiple wirelessantennas of the multiple-input, multiple-output antenna arrangement.

In one or more additional embodiments, obtaining the signal data canfurther comprise extracting an identifier indicative of a type of basisfunction characterizing the non-linear response of the non-lineardevice, and identifying the type of basis function from the identifier.Further, the method can comprise determining an order of the basisfunction and a memory length of the basis function from the signal data,and extracting the coefficients of the basis function from the signaldata. Additionally, the method can comprise reconstructing the basisfunction characterizing the non-linear response of the non-linear devicefrom the type of basis function, the order of the basis function, thememory length of the basis function, and the coefficients of the basisfunction, and utilizing the basis function characterizing the non-linearresponse of the non-linear device in conjunction with the decoding ofthe wireless transmission of the multiple-input, multiple-outputwireless transmission.

In yet another embodiment, receiving the signal can further compriseobtaining the signal data from physical layer signaling transmitted bythe wireless transmitter. For instance, the method can comprisemonitoring the physical layer signaling for a change in the signal datareflecting an update to the non-linear response of the non-linear deviceoperatively coupled with the wireless transmitter.

Referring now to FIG. 11, there is depicted a flowchart of an examplemethod 1100 according to additional embodiments of the presentdisclosure. At 1102, method 1100 can comprise obtaining, by a wirelessreceiver (or receivers) of a system comprising a processor, signal datatransmitted by a MIMO transmission. At 1104, method 1100 can compriseextracting an identifier indicative of a type of basis functioncharacterizing a non-linear response(s) of a non-linear device(s). Thenon-linear response(s) can be a non-linear function(s) of the non-lineardevice(s), which can be connected to one or more active-array-antennassending the MIMO transmission. At 1106, method 1100 can compriseextracting an identifier indicative of a type of basis functioncharacterizing a non-linear response of a non-linear device, at 1106identifying the type of basis function from the identifier, anddetermining an order and a memory length of the basis function from thesignal data at 1108. At 1110, method 1100 can additionally compriseextracting coefficients of the basis function from the signal data.

At 1112, method 1100 can comprise reconstructing the basis functioncharacterizing the non-linear response of the non-linear device from thetype, order, memory length and coefficients of the basis function.Additionally, at 1114, method 1100 can comprise utilizing the basisfunction in conjunction with a maximum likelihood detection of datareceived with the MIMO transmission.

FIG. 12 illustrates a flowchart of an example method 1200 forfacilitating improved wireless transmission, according to additionalembodiments of the present disclosure. In some embodiments, method 1200can be implemented for a multiple antenna transmission. In one or moreembodiments, method 1200 can be implemented in conjunction with amassive MIMO wireless transmission for a 5G wireless communicationnetwork.

At 1202, method 1200 can comprise acquiring signal data pertaining to anon-linear response of a non-linear device operably connected to awireless transmitter. The non-linear response can be a non-linearfunction of a non-linear power amplifier, DAC, oscillator, or the like,connected to a massive MIMO active antenna array, in variousembodiments.

At 1204, method 1200 can comprise forwarding the signal data to areceiving device in conjunction with establishing a wirelesscommunication between the wireless transmitter and the receiving device.The signal data can be transmitted according to a data model. The datamodel can specify a type of basis function employed for fitting thenon-linear response of the non-linear device. Alternatively, oradditionally, the data model can include an index indicative of the typeof basis function. Further, an order, memory length and coefficients forthe data model can be specified within the signal data forwarded to thereceiving device.

FIG. 13 depicts a flowchart of a sample method 1300 for facilitatingimproved bit error rate performance for a wireless transmission,according to additional embodiments of the present disclosure. At 1302,method 1300 can comprise initiating, by a system comprising a processor,a non-linear electronic device in conjunction with a MIMO wirelesstransmission. At 1304, method 1300 can comprise fitting, by the system,a non-linear response of the non-linear electronic device to amathematical model. Moreover, at 1306, method 1300 can comprise writingcoefficients, order and memory length of the mathematical model to adata message. In addition to the foregoing, method 1300 can comprisespecifying a type and index of the mathematical model in the datamessage at 1308. At 1310, method 1300 can comprise transmitting the datamessage to a MIMO receiver device of the MIMO wireless transmission.

Referring now to FIG. 14, illustrated is a schematic block diagram of auser equipment (e.g., UE 102, etc.) that can be a mobile handset 1400capable of connecting to a network in accordance with some embodimentsdescribed herein. Although a mobile handset 1400 is illustrated herein,it will be understood that other devices can be a mobile device, andthat the mobile handset 1400 is merely illustrated to provide contextfor the embodiments of the various embodiments described herein. Thefollowing discussion is intended to provide a brief, general descriptionof an example of a suitable environment 1400 in which the variousembodiments can be implemented. While the description includes a generalcontext of computer-executable instructions embodied on amachine-readable storage medium, those skilled in the art will recognizethat the innovation also can be implemented in combination with otherprogram modules and/or as a combination of hardware and software.

Generally, applications (e.g., program modules) can include routines,programs, components, data structures, etc., that perform particulartasks or implement particular abstract data types. Moreover, thoseskilled in the art will appreciate that the methods described herein canbe practiced with other system configurations, includingsingle-processor or multiprocessor systems, minicomputers, mainframecomputers, as well as personal computers, hand-held computing devices,microprocessor-based or programmable consumer electronics, and the like,each of which can be operatively coupled to one or more associateddevices.

A computing device can typically include a variety of machine-readablemedia. Machine-readable media can be any available media that can beaccessed by the computer and includes both volatile and non-volatilemedia, removable and non-removable media. By way of example and notlimitation, computer-readable media can comprise computer storage mediaand communication media. Computer storage media can include volatileand/or non-volatile media, removable and/or non-removable mediaimplemented in any method or technology for storage of information, suchas computer-readable instructions, data structures, program modules orother data. Computer storage media can include, but is not limited to,RAM, ROM, EEPROM, flash memory or other memory technology, CD ROM,digital video disk (DVD) or other optical disk storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by the computer.

Communication media typically embodies computer-readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism, and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of the anyof the above should also be included within the scope ofcomputer-readable media.

The mobile handset 1400 includes a processor 1402 for controlling andprocessing all onboard operations and functions. A memory 1404interfaces to the processor 1402 for storage of data and one or moreapplications 1406 (e.g., a video player software, user feedbackcomponent software, etc.). Other applications can include voicerecognition of predetermined voice commands that facilitate initiationof the user feedback signals. The applications 1406 can be stored in thememory 1404 or in a firmware 1408, and executed by the processor 1402from either or both the memory 1404 or the firmware 1408. The firmware1408 can also store startup code for execution in initializing mobilehandset 1400. A communications component 1410 interfaces to theprocessor 1402 to facilitate wired/wireless communication with externalsystems, e.g., cellular networks, VoIP networks, and so on. Here, thecommunications component 1410 can also include a suitable cellulartransceiver 1411 (e.g., a global GSM transceiver) and/or an unlicensedtransceiver 1413 (e.g., Wi-Fi, WiMax) for corresponding signalcommunications. The mobile handset 1400 can be a device such as acellular telephone, a PDA with mobile communications capabilities, andmessaging-centric devices. The communications component 1410 alsofacilitates communications reception from terrestrial radio networks(e.g., broadcast), digital satellite radio networks, and Internet-basedradio services networks.

The mobile handset 1400 includes a display 1412 for displaying text,images, video, telephony functions (e.g., a Caller ID function), setupfunctions, and for user input. For example, the display 1412 can also bereferred to as a “screen” that can accommodate the presentation ofmultimedia content (e.g., music metadata, messages, wallpaper, graphics,etc.). The display 1412 can also display videos and can facilitate thegeneration, editing and sharing of video quotes. A serial I/O interface1414 is provided in communication with the processor 1402 to facilitatewired or wireless serial communications (e.g., USB, and/or IEEE 1394)through a hardwire connection, and other serial input devices (e.g., akeyboard, keypad, and mouse). This supports updating and troubleshootingthe mobile handset 1400, for example. Audio capabilities are providedwith an audio I/O component 1416, which can include a speaker for theoutput of audio signals related to, for example, indication that theuser pressed the proper key or key combination to initiate the userfeedback signal. The audio I/O component 1416 also facilitates the inputof audio signals through a microphone to record data and/or telephonyvoice data, and for inputting voice signals for telephone conversations.

The mobile handset 1400 can include a slot interface 1418 foraccommodating a SIC (Subscriber Identity Component) in the form factorof a card Subscriber Identity Module (SIM) or universal SIM 1420, andinterfacing the SIM card 1420 with the processor 1402. However, it is tobe appreciated that the SIM card 1420 can be manufactured into themobile handset 1400, and updated by downloading data and software.

The mobile handset 1400 can process IP data traffic through thecommunication component 1410 to accommodate IP traffic from an IPnetwork such as, for example, the Internet, a corporate intranet, a homenetwork, a person area network, etc., through an ISP or broadband cableprovider. Thus, VoIP traffic can be utilized by mobile handset 1400 andIP-based multimedia content can be received in either an encoded ordecoded format.

A video processing component 1422 (e.g., a camera) can be provided fordecoding encoded multimedia content. The video processing component 1422can aid in facilitating the generation, editing and sharing of videoquotes. The mobile handset 1400 also includes a power source 1424 in theform of batteries or an AC power subsystem, which power source 1424 caninterface to an external power system or charging equipment (not shown)by a power I/O component 1426.

The mobile handset 1400 can also include a video component 1430 forprocessing video content received and, for recording and transmittingvideo content. For example, the video component 1430 can facilitate thegeneration, editing and sharing of video quotes. A location trackingcomponent 1432 facilitates geographically locating the mobile handset1400. As described hereinabove, this can occur when the user initiatesthe feedback signal automatically or manually. A user input component1434 facilitates the user initiating the quality feedback signal. Theuser input component 1434 can also facilitate the generation, editingand sharing of video quotes. The user input component 1434 can includesuch conventional input device technologies such as a keypad, keyboard,mouse, stylus pen, or touch screen, for example.

Referring again to the applications 1406, a hysteresis component 1436facilitates the analysis and processing of hysteresis data, which isutilized to determine when to associate with the access point. Asoftware trigger component 1438 can be provided that facilitatestriggering of the hysteresis component 1438 when the Wi-Fi transceiver1413 detects the beacon of the access point. A session initiationprotocol (SIP) client 1440 enables the handset 1400 to support SIPprotocols and register the subscriber with the SIP registrar server. Theapplications 1406 can also include a client 1442 that provides at leastthe capability of discovery, play and store of multimedia content, forexample, music.

The mobile handset 1400, as indicated above related to thecommunications component 1410, includes an indoor network radiotransceiver 1413 (e.g., Wi-Fi transceiver). This function supports theindoor radio link, such as IEEE 802.11, for mobile handset 1400. Themobile handset 1400 can accommodate at least satellite radio servicesthrough a handset that can combine wireless voice and digital radiochipsets into a single handheld device.

Referring now to FIG. 15, there is illustrated a block diagram of acomputer 1500 operable to execute the functions and operations performedin the described example embodiments. For example, a network node (e.g.,network node 104) may contain components as described in FIG. 15. Thecomputer 1500 can provide networking and communication capabilitiesbetween a wired or wireless communication network and a server orcommunication device. In order to provide additional context for variousaspects thereof, FIG. 15 and the following discussion are intended toprovide a brief, general description of a suitable computing environmentin which the various aspects of the innovation can be implemented tofacilitate the establishment of a transaction between an entity and athird party. While the description above is in the general context ofcomputer-executable instructions that can run on one or more computers,those skilled in the art will recognize that the innovation also can beimplemented in combination with other program modules or as acombination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

The illustrated aspects of the innovation can also be practiced indistributed computing environments where certain tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media or communications media, whichtwo terms are used herein differently from one another as follows.

Computer-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media can include,but are not limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible and/or non-transitorymedia which can be used to store desired information. Computer-readablestorage media can be accessed by one or more local or remote computingdevices, e.g., via access requests, queries or other data retrievalprotocols, for a variety of operations with respect to the informationstored by the medium.

Communications media can embody computer-readable instructions, datastructures, program modules or other structured or unstructured data ina data signal such as a modulated data signal, e.g., a carrier wave orother transport mechanism, and includes any information delivery ortransport media. The term “modulated data signal” or signals refers to asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in one or more signals. By way ofexample, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference to FIG. 15, implementing various aspects described hereinwith regards to devices (e.g., network node 104, UE 102 _(1-n)non-linear model identifier 210, among others) can include a computer1500, the computer 1500 including a processing unit 1504, a systemmemory 1506 and a system bus 1508. The system bus 1508 couples systemcomponents including, but not limited to, the system memory 1506 to theprocessing unit 1504. The processing unit 1504 can be any of variouscommercially available processors. Dual microprocessors and othermulti-processor architectures can also be employed as the processingunit 1504.

The system bus 1508 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1506includes read-only memory (ROM) 1527 and random access memory (RAM)1512. A basic input/output system (BIOS) is stored in a non-volatilememory 1527 such as ROM, EPROM, EEPROM, which BIOS contains the basicroutines that help to transfer information between elements within thecomputer 1500, such as during start-up. The RAM 1512 can also include ahigh-speed RAM such as static RAM for caching data.

The computer 1500 further includes an internal hard disk drive (HDD)1514 (e.g., EIDE, SATA), which internal hard disk drive 1514 can also beconfigured for external use in a suitable chassis (not shown), amagnetic floppy disk drive (FDD) 1516, (e.g., to read from or write to aremovable diskette 1518) and an optical disk drive 1520, (e.g., readinga CD-ROM disk 1522 or, to read from or write to other high capacityoptical media such as the DVD). The hard disk drive 1514, magnetic diskdrive 1516 and optical disk drive 1520 can be connected to the systembus 1508 by a hard disk drive interface 1524, a magnetic disk driveinterface 1526 and an optical drive interface 1528, respectively. Theinterface 1524 for external drive implementations includes at least oneor both of Universal Serial Bus (USB) and IEEE 1394 interfacetechnologies. Other external drive connection technologies are withincontemplation of the subject innovation.

The drives and their associated computer-readable media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1500, the drives and mediaaccommodate the storage of any data in a suitable digital format.Although the description of computer-readable media above refers to aHDD, a removable magnetic diskette, and a removable optical media suchas a CD or DVD, it should be appreciated by those skilled in the artthat other types of media which are readable by a computer 1500, such aszip drives, magnetic cassettes, flash memory cards, cartridges, and thelike, can also be used in the example operating environment, andfurther, that any such media can contain computer-executableinstructions for performing the methods of the disclosed innovation.

A number of program modules can be stored in the drives and RAM 1512,including an operating system 1530, one or more application programs1532, other program modules 1534 and program data 1536. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1512. It is to be appreciated that the innovation canbe implemented with various commercially available operating systems orcombinations of operating systems.

A user can enter commands and information into the computer 1500 throughone or more wired/wireless input devices, e.g., a keyboard 1538 and apointing device, such as a mouse 1540. Other input devices (not shown)may include a microphone, an IR remote control, a joystick, a game pad,a stylus pen, touch screen, or the like. These and other input devicesare often connected to the processing unit 1504 through an input deviceinterface 1542 that is coupled to the system bus 1508, but can beconnected by other interfaces, such as a parallel port, an IEEE 1394serial port, a game port, a USB port, an IR interface, etc.

A monitor 1544 or other type of display device is also connected to thesystem bus 1508 through an interface, such as a video adapter 1546. Inaddition to the monitor 1544, a computer 1500 typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1500 can operate in a networked environment using logicalconnections by wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1548. The remotecomputer(s) 1548 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentdevice, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer,although, for purposes of brevity, only a memory/storage device 1550 isillustrated. The logical connections depicted include wired/wirelessconnectivity to a local area network (LAN) 1552 and/or larger networks,e.g., a wide area network (WAN) 1554. Such LAN and WAN networkingenvironments are commonplace in offices and companies, and facilitateenterprise-wide computer networks, such as intranets, all of which mayconnect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 1500 isconnected to the local network 1552 through a wired and/or wirelesscommunication network interface or adapter 1556. The adapter 1556 mayfacilitate wired or wireless communication to the LAN 1552, which mayalso include a wireless access point disposed thereon for communicatingwith the wireless adapter 1556.

When used in a WAN networking environment, the computer 1500 can includea modem 1558, or is connected to a communications server on the WAN1554, or has other means for establishing communications over the WAN1554, such as by way of the Internet. The modem 1558, which can beinternal or external and a wired or wireless device, is connected to thesystem bus 1508 through the input device interface 1542. In a networkedenvironment, program modules depicted relative to the computer, orportions thereof, can be stored in the remote memory/storage device1550. It will be appreciated that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers can be used.

The computer is operable to communicate with any wireless devices orentities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, any piece of equipment or locationassociated with a wirelessly detectable tag (e.g., a kiosk, news stand,restroom), and telephone. This includes at least Wi-Fi and Bluetooth™wireless technologies. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices.

Wi-Fi, or Wireless Fidelity, allows connection to the Internet from acouch at home, a bed in a hotel room, or a conference room at work,without wires. Wi-Fi is a wireless technology similar to that used in acell phone that enables such devices, e.g., computers, to send andreceive data indoors and out; anywhere within the range of a basestation. Wi-Fi networks use radio technologies called IEEE802.11 (a, b,g, n, etc.) to provide secure, reliable, fast wireless connectivity. AWi-Fi network can be used to connect computers to each other, to theInternet, and to wired networks (which use IEEE802.3 or Ethernet). Wi-Finetworks operate in the unlicensed 2.4 and 5 GHz radio bands, at an 11Mbps (802.11b) or 54 Mbps (802.11a) data rate, for example, or withproducts that contain both bands (dual band), so the networks canprovide real-world performance similar to the basic “10BaseT” wiredEthernet networks used in many offices.

As used in this application, the terms “system,” “component,”“interface,” and the like are generally intended to refer to acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component may be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. These components also can execute from various computerreadable storage media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry that is operated bysoftware or firmware application(s) executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. An interface can comprise input/output (I/O)components as well as associated processor, application, and/or APIcomponents.

Furthermore, the disclosed subject matter may be implemented as amethod, apparatus, or article of manufacture using standard programmingand/or engineering techniques to produce software, firmware, hardware,or any combination thereof to control a computer to implement thedisclosed subject matter. The term “article of manufacture” as usedherein is intended to encompass a computer program accessible from anycomputer-readable device, computer-readable carrier, orcomputer-readable media. For example, computer-readable media caninclude, but are not limited to, a magnetic storage device, e.g., harddisk; floppy disk; magnetic strip(s); an optical disk (e.g., compactdisk (CD), a digital video disc (DVD), a Blu-ray Disc™ (BD)); a smartcard; a flash memory device (e.g., card, stick, key drive); and/or avirtual device that emulates a storage device and/or any of the abovecomputer-readable media.

As it employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to comprising, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Processors can exploit nano-scale architectures suchas, but not limited to, molecular and quantum-dot based transistors,switches and gates, in order to optimize space usage or enhanceperformance of user equipment. A processor also can be implemented as acombination of computing processing units.

In the subject specification, terms such as “store,” “data store,” “datastorage,” “database,” “repository,” “queue”, and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory. In addition, memory components or memory elementscan be removable or stationary. Moreover, memory can be internal orexternal to a device or component, or removable or stationary. Memorycan comprise various types of media that are readable by a computer,such as hard-disc drives, zip drives, magnetic cassettes, flash memorycards or other types of memory cards, cartridges, or the like.

By way of illustration, and not limitation, nonvolatile memory cancomprise read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable ROM (EEPROM), or flashmemory. Volatile memory can comprise random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), anddirect Rambus RAM (DRRAM). Additionally, the disclosed memory componentsof systems or methods herein are intended to comprise, without beinglimited to comprising, these and any other suitable types of memory.

In particular and in regard to the various functions performed by theabove described components, devices, circuits, systems and the like, theterms (including a reference to a “means”) used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., a functional equivalent), even though not structurallyequivalent to the disclosed structure, which performs the function inthe herein illustrated example aspects of the embodiments. In thisregard, it will also be recognized that the embodiments comprise asystem as well as a computer-readable medium having computer-executableinstructions for performing the acts or events of the various methods.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media cancomprise, but are not limited to, RAM, ROM, EEPROM, flash memory orother memory technology, CD-ROM, digital versatile disk (DVD) or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or other tangible and/ornon-transitory media which can be used to store desired information.Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

On the other hand, communications media typically embodycomputer-readable instructions, data structures, program modules orother structured or unstructured data in a data signal such as amodulated data signal, e.g., a carrier wave or other transportmechanism, and comprises any information delivery or transport media.The term “modulated data signal” or signals refers to a signal that hasone or more of its characteristics set or changed in such a manner as toencode information in one or more signals. By way of example, and notlimitation, communications media comprise wired media, such as a wirednetwork or direct-wired connection, and wireless media such as acoustic,RF, infrared and other wireless media.

Further, terms like “user equipment,” “user device,” “mobile device,”“mobile,” station,” “access terminal,” “terminal,” “handset,” andsimilar terminology, generally refer to a wireless device utilized by asubscriber or user of a wireless communication network or service toreceive or convey data, control, voice, video, sound, gaming, orsubstantially any data-stream or signaling-stream. The foregoing termsare utilized interchangeably in the subject specification and relateddrawings. Likewise, the terms “access point,” “node B,” “base station,”“evolved Node B,” “cell,” “cell site,” and the like, can be utilizedinterchangeably in the subject application, and refer to a wirelessnetwork component or appliance that serves and receives data, control,voice, video, sound, gaming, or substantially any data-stream orsignaling-stream from a set of subscriber stations. Data and signalingstreams can be packetized or frame-based flows. It is noted that in thesubject specification and drawings, context or explicit distinctionprovides differentiation with respect to access points or base stationsthat serve and receive data from a mobile device in an outdoorenvironment, and access points or base stations that operate in aconfined, primarily indoor environment overlaid in an outdoor coveragearea. Data and signaling streams can be packetized or frame-based flows.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer,” andthe like are employed interchangeably throughout the subjectspecification, unless context warrants particular distinction(s) amongthe terms. It should be appreciated that such terms can refer to humanentities, associated devices, or automated components supported throughartificial intelligence (e.g., a capacity to make inference based oncomplex mathematical formalisms) which can provide simulated vision,sound recognition and so forth. In addition, the terms “wirelessnetwork” and “network” are used interchangeable in the subjectapplication, when context wherein the term is utilized warrantsdistinction for clarity purposes such distinction is made explicit.

Moreover, the word “exemplary” where used herein means serving as anexample, instance, or illustration. Any aspect or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Rather, use of the wordexemplary is intended to present concepts in a concrete fashion. As usedin this application, the term “or” is intended to mean an inclusive “or”rather than an exclusive “or”. That is, unless specified otherwise, orclear from context, “X employs A or B” is intended to mean any of thenatural inclusive permutations. That is, if X employs A; X employs B; orX employs both A and B, then “X employs A or B” is satisfied under anyof the foregoing instances. In addition, the articles “a” and “an” asused in this application and the appended claims should generally beconstrued to mean “one or more” unless specified otherwise or clear fromcontext to be directed to a singular form.

In addition, while a particular feature may have been disclosed withrespect to only one of several implementations, such feature may becombined with one or more other features of the other implementations asmay be desired and advantageous for any given or particular application.Furthermore, to the extent that the terms “includes” and “including” andvariants thereof are used in either the detailed description or theclaims, these terms are intended to be inclusive in a manner similar tothe term “comprising.”

The above descriptions of various embodiments of the subject disclosureand corresponding figures and what is described in the Abstract, aredescribed herein for illustrative purposes, and are not intended to beexhaustive or to limit the disclosed embodiments to the precise formsdisclosed. It is to be understood that one of ordinary skill in the artmay recognize that other embodiments having modifications, permutations,combinations, and additions can be implemented for performing the same,similar, alternative, or substitute functions of the disclosed subjectmatter, and are therefore considered within the scope of thisdisclosure. Therefore, the disclosed subject matter should not belimited to any single embodiment described herein, but rather should beconstrued in breadth and scope in accordance with the claims below.

What is claimed is:
 1. A system, comprising: a processor; and a memorythat stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: in responseto activation of a non-linear device of a device operatively connectedto a wireless transmitter, acquiring signal data pertaining to acharacterization of a non-linear power response of the non-lineardevice; and in response to establishing a wireless communication betweenthe wireless transmitter and a receiving device, forwarding the signaldata pertaining to the characterization of the non-linear response ofthe non-linear device to the receiving device, wherein the wirelesstransmitter comprises a wireless antenna element operative tocommunicate using wireless signals.
 2. The system of claim 1, whereinthe wireless transmitter comprises wireless antenna elements, comprisingthe wireless antenna element, operable in a multiple-input,multiple-output arrangement for wireless communication with thereceiving device.
 3. The system of claim 2, wherein the operationsfurther comprise: acquiring respective signal data pertaining tonon-linear power responses of respective non-linear devices operativelyconnected to the wireless antenna elements.
 4. The system of claim 3,wherein the operations further comprise aggregating the respectivesignal data of the respective non-linear devices for decoding ofwireless communications at the receiving device transmitted by thewireless antenna elements, resulting in aggregated respective signaldata, and wherein the operations further comprise forwarding theaggregated respective signal data to the receiving device in conjunctionwith the wireless communication between the wireless antenna elementsand the receiving device being established.
 5. The system of claim 2,wherein the operations further comprise acquiring the signal data from afirst group of non-linear devices respectively operatively connected tothe wireless antenna elements, and utilizing the signal data to estimatea non-linear signal response of a second group of the non-lineardevices.
 6. The system of claim 1, wherein the non-linear devicecomprises a power amplifier.
 7. The system of claim 1, wherein theoperations further comprise mapping a basis function to the non-linearpower response of the non-linear device.
 8. The system of claim 7,wherein the signal data comprises a coefficient of the basis functionmapped to the non-linear power response.
 9. The system of claim 7,wherein the basis function comprises a polynomial expansion fit betweenan input signal and an output signal of the non-linear device utilizingat least one of: a minimization criterion solving a least squares fitbetween the output signal and the input signal, or a Bayesian analysisof the non-linear power response employing Kalman filtering.
 10. Thesystem of claim 1, wherein the forwarding comprises forwarding thesignal data to the receiving device utilizing a signaling layerassociated with a setup of the wireless communication between thewireless transmitter and the receiving device.
 11. The system of claim1, wherein the forwarding comprises forwarding the signal data to thereceiving device utilizing a physical layer of a wireless channel. 12.The system of claim 1, wherein the signal data comprises: an indexidentifying a type of basis function fit to the non-linear powerresponse, an order of the basis function, a memory length of the signaldata, and a coefficient of the basis function.
 13. A method, comprising:obtaining, by a wireless receiver of a system comprising a processor,signal data transmitted by a wireless transmitter indicative of acharacterization of a non-linear response of a non-linear deviceoperatively coupled with the wireless transmitter; and decoding, by thesystem, a wireless transmission of the wireless transmitter comprisingutilizing the signal data indicative of the characterization of thenon-linear response of the non-linear device, wherein the wirelesstransmitter is a member of a multiple-input, multiple output antennaarrangement comprising multiple wireless antennas, and wherein thewireless transmission is a multiple-input, multiple output wirelesstransmission implemented by the multiple wireless antennas.
 14. Themethod of claim 13, wherein the decoding the wireless transmissionfurther comprises utilizing the signal data in conjunction with amaximum likelihood function to determine data received with the wirelesstransmission, wherein the signal data comprises coefficients of a basisfunction fit to the non-linear response of the non-linear device at thewireless transmitter, and wherein the method further comprisesreconstructing, by the system, the basis function fit to the non-linearresponse utilizing the coefficients of the basis function.
 15. Themethod of claim 13, further comprising obtaining, by the system, thesignal data from call setup signaling associated with initiation of thewireless transmission.
 16. The method of claim 13, wherein the signaldata represents an aggregate of non-linear responses of a group ofnon-linear devices, comprising the non-linear device, respectivelyoperatively coupled to the multiple wireless antennas of themultiple-input, multiple output antenna arrangement.
 17. The method ofclaim 13, wherein obtaining the signal data comprises: extracting anidentifier indicative of a type of basis function for thecharacterization of the non-linear response of the non-linear device,identifying the type of basis function from the identifier, determiningan order of the basis function and a memory length of the basis functionfrom the signal data, extracting coefficients of the basis function fromthe signal data, reconstructing the basis function for thecharacterization of the non-linear response of the non-linear devicefrom the type of basis function, the order of the basis function, thememory length of the basis function, and the coefficients of the basisfunction, and utilizing the basis function for the characterization ofthe non-linear response of the non-linear device in conjunction with thedecoding of the wireless transmission of the multiple-input, multipleoutput wireless transmission.
 18. A non-transitory machine-readablestorage medium, comprising executable instructions that, when executedby a processor of a wireless system, facilitate performance ofoperations, comprising: receiving, from a wireless transmitter, signaldata indicative of a model of a non-linear response of a non-lineardevice operatively coupled with the wireless transmitter; and decoding awireless transmission of the wireless transmitter comprising utilizingthe signal data indicative of the model of the non-linear response ofthe non-linear device, wherein the wireless transmitter is part ofmultiple wireless antennas configured to transmit according to amultiple-input, multiple-output wireless protocol, and wherein thewireless transmission is transmitted according to the multiple-input,multiple output wireless protocol.
 19. The non-transitorymachine-readable storage medium of claim 18, wherein the receiving thesignal data comprises obtaining the signal data from physical layersignaling that was transmitted by the wireless transmitter.
 20. Thenon-transitory machine-readable storage medium of claim 19, wherein theoperations further comprise: monitoring the physical layer signaling fora change in the signal data reflecting an update to the non-linearresponse of the non-linear device operatively coupled with the wirelesstransmitter; determining an updated non-linear response from the changein the signal data; and decoding subsequent transmissions of thewireless transmission of the wireless transmitter utilizing the updatednon-linear response.